Résumé IA · arXiv: 2305.02328v1 · cs.AI
Cultivated Wildness: Technodiversity and Wildness in Machines
Posez des questions sur cet article à notre assistant IA
Comment discuter avec cet article ? Guide illustré
Vous pouvez aussi discutez avec plusieurs papiers à la fois ici.
Gardez cette conversation — un compte gratuit conserve vos questions et réponses, vos collections d’articles et vos alertes.
Continuer avec Google Créer un compte gratuitOutils pour cet article
Outils avancés Premium
Pour suivre un domaine de près : alertes de citations, diapositives de journal club, comparaison des versions et envoi vers Zotero. Découvrir Premium
Les articles autour de celui-ci
Les voir sur une carteClassés selon la proximité de leur titre et de leur résumé avec ceux de cet article. Sélectionnez-en pour les lire ensemble.
Les articles les plus proches
-
Societal and technological progress as sewing an ever-growing, ever-changing, patchy, and polychrome quiltJoel Z. Leibo, Alexander Sasha Vezhnevets, William A. Cunningham et al. · 2025 · Intelligence artificielle · Résumé IA disponible
Résumé scientifique
Artificial Intelligence (AI) systems are increasingly placed in positions where their decisions have real consequences, e.g., moderating online spaces, conducting research, and advising on policy. Ensuring they operate in a safe and ethically acceptable fashion is thus critical. However, most solutions have been a form of one-size-fits-all "alignment". We are worried that such systems, which overlook enduring moral diversity, will spark resistance, erode trust, and destabilize our institutions. This paper traces the underlying problem to an often-unstated Axiom of Rational Convergence: the idea that under ideal conditions, rational agents will converge in the limit of conversation on a singl…
-
A Survey on AI Sustainability: Emerging Trends on Learning Algorithms and Research ChallengesZhenghua Chen, Min Wu, Alvin Chan et al. · 2022 · Intelligence artificielle · Résumé IA disponible
Résumé scientifique
Artificial Intelligence (AI) is a fast-growing research and development (R&D) discipline which is attracting increasing attention because of its promises to bring vast benefits for consumers and businesses, with considerable benefits promised in productivity growth and innovation. To date it has reported significant accomplishments in many areas that have been deemed as challenging for machines, ranging from computer vision, natural language processing, audio analysis to smart sensing and many others. The technical trend in realizing the successes has been towards increasing complex and large size AI models so as to solve more complex problems at superior performance and robustness. This rap…
-
Towards Automated Urban Planning: When Generative and ChatGPT-like AI Meets Urban PlanningDongjie Wang, Chang-Tien Lu, Yanjie Fu · 2023 · Intelligence artificielle · Résumé IA disponible
Résumé scientifique
The two fields of urban planning and artificial intelligence (AI) arose and developed separately. However, there is now cross-pollination and increasing interest in both fields to benefit from the advances of the other. In the present paper, we introduce the importance of urban planning from the sustainability, living, economic, disaster, and environmental perspectives. We review the fundamental concepts of urban planning and relate these concepts to crucial open problems of machine learning, including adversarial learning, generative neural networks, deep encoder-decoder networks, conversational AI, and geospatial and temporal machine learning, thereby assaying how AI can contribute to mode…
-
Intersymbolic AI: Interlinking Symbolic AI and Subsymbolic AIAndré Platzer · 2024 · Intelligence artificielle · Résumé IA disponible
Résumé scientifique
This perspective piece calls for the study of the new field of Intersymbolic AI, by which we mean the combination of symbolic AI, whose building blocks have inherent significance/meaning, with subsymbolic AI, whose entirety creates significance/effect despite the fact that individual building blocks escape meaning. Canonical kinds of symbolic AI are logic, games and planning. Canonical kinds of subsymbolic AI are (un)supervised machine and reinforcement learning. Intersymbolic AI interlinks the worlds of symbolic AI with its compositional symbolic significance and meaning and of subsymbolic AI with its summative significance or effect to enable culminations of insights from both worlds by go…
-
Agentic AI and the next intelligence explosionJames Evans, Benjamin Bratton, Blaise Agüera y Arcas · 2026 · Intelligence artificielle · Résumé IA disponible
Résumé scientifique
The "AI singularity" is often miscast as a monolithic, godlike mind. Evolution suggests a different path: intelligence is fundamentally plural, social, and relational. Recent advances in agentic AI reveal that frontier reasoning models, such as DeepSeek-R1, do not improve simply by "thinking longer". Instead, they simulate internal "societies of thought," spontaneous cognitive debates that argue, verify, and reconcile to solve complex tasks. Moreover, we are entering an era of human-AI centaurs: hybrid actors where collective agency transcends individual control. Scaling this intelligence requires shifting from dyadic alignment (RLHF) toward institutional alignment. By designing digital prot…
-
Scope for Machine Learning in Digital ManufacturingMartin Baumers, Ender Ozcan · 2016 · Intelligence artificielle · Résumé IA disponible
Résumé scientifique
This provocation paper provides an overview of the underlying optimisation problem in the emerging field of Digital Manufacturing. Initially, this paper discusses how the notion of Digital Manufacturing is transforming from a term describing a suite of software tools for the integration of production and design functions towards a more general concept incorporating computerised manufacturing and supply chain processes, as well as information collection and utilisation across the product life cycle. On this basis, we use the example of one such manufacturing process, Additive Manufacturing, to identify an integrated multi-objective optimisation problem underlying Digital Manufacturing. Formin…
-
A Framework for Searching for General Artificial IntelligenceMarek Rosa, Jan Feyereisl, The GoodAI Collective · 2016 · Intelligence artificielle · Résumé IA disponible
Résumé scientifique
There is a significant lack of unified approaches to building generally intelligent machines. The majority of current artificial intelligence research operates within a very narrow field of focus, frequently without considering the importance of the 'big picture'. In this document, we seek to describe and unify principles that guide the basis of our development of general artificial intelligence. These principles revolve around the idea that intelligence is a tool for searching for general solutions to problems. We define intelligence as the ability to acquire skills that narrow this search, diversify it and help steer it to more promising areas. We also provide suggestions for studying, mea…
-
Digital Twin Framework for Optimal and Autonomous Decision-Making in Cyber-Physical Systems: Enhancing Reliability and Adaptability in the Oil and Gas IndustryCarine Menezes Rebello, Johannes Jäschkea, Idelfonso B. R. Nogueira · 2023 · Intelligence artificielle · Résumé IA disponible
Résumé scientifique
The concept of creating a virtual copy of a complete Cyber-Physical System opens up numerous possibilities, including real-time assessments of the physical environment and continuous learning from the system to provide reliable and precise information. This process, known as the twinning process or the development of a digital twin (DT), has been widely adopted across various industries. However, challenges arise when considering the computational demands of implementing AI models, such as those employed in digital twins, in real-time information exchange scenarios. This work proposes a digital twin framework for optimal and autonomous decision-making applied to a gas-lift process in the oil…
Aller plus loin
Comment ces articles sont-ils trouvés ?
Nous comparons le titre et le résumé de cet article avec ceux de tous les articles résumés sur ce site, et avec les articles que Semantic Scholar recommande, ceux qu’il cite et ceux qui le citent (pour un article très cité, ses citations les plus récentes et, depuis OpenAlex, les plus citées). Parmi les travaux antérieurs et les suites, les articles qui s’utilisent en profondeur, une méthode qu’il utilise et les suites très citées remontent un peu. Les niveaux (très proche, proche, apparenté) viennent de la similarité des textes, pas d’un jugement de qualité ; * signale un article sans résumé, comparé par son titre. La phrase sous un article est écrite par une IA à partir des deux résumés : vérifiez-la dans les articles. Un article jugé hors sujet par trois lecteurs quitte la liste.
Pour l’instant seulement parmi les articles résumés sur ce site : relancez la recherche pour ajouter les travaux antérieurs, les suites et les recommandations de Semantic Scholar.
Le résumé précédent a été créé il y a plus d'un an et peut être réexécuté (si nécessaire) en cliquant sur le bouton Exécuter ci-dessous.
Avertissement : Notre outil de synthèse basé sur l'IA et l'assistant virtuel fournis sur ce site Web peuvent ne pas toujours fournir des résumés complets ou des réponses exactes. Nous vous encourageons à examiner attentivement et à évaluer le contenu généré pour vous assurer de sa qualité et de sa pertinence par rapport à vos besoins.